Abstract
In this paper, a data-oriented model has been presented by nonlinear autoregressive exogenous model (NARX) neural network, which aims at predicting the mechanical behavior of a fuel cell stack for vehicle under the real-life operational conditions. A 300-hour vibration test with reproduction of SVP road spectrum was completed on a Multi-Axial Simulation Table. At the same time, data acquisition of drive displacement and acceleration response on stack was carried out in every 50 hours. All data collected were used to train and evaluate the model based on NARX. Result shows that the prediction model built is of good precision and consistent with the actual situation.
Highlights
With the development of technology, economy, and people’s living standard, the number of automobiles has been increased sharply, which brought about energy shortage and air pollution
According to the policy called Made in China 2025 and the US Department of Energy (DOE), lifetime of fuel cell vehicles needs to reach 5000 h by 2020 [1]
Literatures mentioned above verify the superiority of nonlinear autoregressive exogenous model (NARX) neural network in dealing with different kinds of time series datasets and investigate the mechanical characteristics as well as modeling of aircraft fuel cell stacks under dynamic loads
Summary
With the development of technology, economy, and people’s living standard, the number of automobiles has been increased sharply, which brought about energy shortage and air pollution. Rouss [11,12,13,14] proposed a new modeling approach for fuel cell stack, which is of complex nonlinear nature They investigated the mechanical characteristics of fuel cell stacks under vibrating conditions in aircraft application from two parts: experiment and modeling. They set up a vibration test bench. Literatures mentioned above verify the superiority of NARX neural network in dealing with different kinds of time series datasets and investigate the mechanical characteristics as well as modeling of aircraft fuel cell stacks under dynamic loads. The model was trained by the training sets; the prediction performance was validated from the following three aspects: the contrast analysis for network outputs and target values in both time domain and frequency domain, the correlation test, and the eigenvalue test
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